def saveNPZ(): mkdir_p(npPath) datany=numpy.uint8(numpy.array(totalNydata)) lableny=numpy.uint8(numpy.array(totallNyables)) numpy.savez(npPath+str(lable)+".npz",data6=datany,lable6=lableny) print(str(lable)+"号数据保存")
时间: 2024-04-18 11:24:23 浏览: 144
这段代码看起来是一个用于保存数据到 npz 文件的函数。让我解释一下代码的逻辑:
1. `mkdir_p(npPath)`:这是一个自定义函数,用于创建文件夹。它会创建一个名为 `npPath` 的文件夹,如果该文件夹已经存在,则不会进行任何操作。
2. `datany = numpy.uint8(numpy.array(totalNydata))`:这行代码将 `totalNydata` 转换为一个 numpy 数组,并将其类型转换为 `uint8`。
3. `lableny = numpy.uint8(numpy.array(totallNyables))`:这行代码将 `totallNyables` 转换为一个 numpy 数组,并将其类型转换为 `uint8`。
4. `numpy.savez(npPath+str(lable)+".npz", data6=datany, lable6=lableny)`:这行代码将两个数组 `datany` 和 `lableny` 保存为一个 npz 文件。文件名由 `npPath`、`lable` 和 ".npz" 组成。数组被保存为名为 "data6" 和 "lable6" 的键值对。
5. `print(str(lable) + "号数据保存")`:这行代码用于打印保存数据的信息,包括数据编号。
总的来说,这段代码的功能是将两个数组保存为 npz 文件,并打印保存信息。
相关问题
将下面这段代码改用python写出来: clear all; close all; fdir = '../dataset/iso/saii/'; %Reconstruction parameters depth_start = 710; depth_end = 720; depth_step = 1; pitch = 12; sensor_sizex = 24; focal_length = 8; lens_x = 4; lens_y = 4; %% import elemental image infile=[fdir '11.bmp']; outfile=[fdir, 'EIRC/']; mkdir(outfile); original_ei=uint8(imread(infile)); [v,h,d]=size(original_ei); %eny = v/lens_y; enx = h/lens_x; % Calculate real focal length %f_ratio=36/sensor_sizex; sensor_sizey = sensor_sizex * (v/h); %focal_length = focal_length*f_ratio; EI = zeros(v, h, d, lens_x * lens_y,'uint8'); for y = 1:lens_y for x = 1:lens_x temp=imread([fdir num2str(y),num2str(x),'.bmp']); EI(:, :, :, x + (y-1) * lens_y) = temp; end end %Reconstruction [EIy, EIx, Color] = size(EI(:,:,:,1)); %% EI_VCR time=[]; for Zr = depth_start:depth_step:depth_end tic; Shx = 8*round((EIx*pitch*focal_length)/(sensor_sizex*Zr)); Shy = 8*round((EIy*pitch*focal_length)/(sensor_sizey*Zr)); Img = (double(zeros(EIy+(lens_y-1)*Shy,EIx+(lens_x-1)*Shx, Color))); Intensity = (uint16(zeros(EIy+(lens_y-1)*Shy,EIx+(lens_x-1)*Shx, Color))); for y=1:lens_y for x=1:lens_x Img((y-1)*Shy+1:(y-1)*Shy+EIy,(x-1)*Shx+1:(x-1)*Shx+EIx,:) = Img((y-1)*Shy+1:(y-1)*Shy+EIy,(x-1)*Shx+1:(x-1)*Shx+EIx,:) + im2double(EI(:,:,:,x+(y-1)*lens_y)); Intensity((y-1)*Shy+1:(y-1)*Shy+EIy,(x-1)*Shx+1:(x-1)*Shx+EIx,:) = Intensity((y-1)*Shy+1:(y-1)*Shy+EIy,(x-1)*Shx+1:(x-1)*Shx+EIx,:) + uint16(ones(EIy,EIx,Color)); end end elapse=toc time=[time elapse]; display(['--------------- Z = ', num2str(Zr), ' is processed ---------------']); Fname = sprintf('EIRC/%dmm.png',Zr); imwrite(Img./double(Intensity), [fdir Fname]); end csvwrite([fdir 'EIRC/time.csv'],time);
下面是将给定代码段转换为Python的等效代码:
```python
import os
import numpy as np
import cv2
# Clear all variables
# Close all figures
# These are not necessary in Python
fdir = '../dataset/iso/saii/'
# Reconstruction parameters
depth_start = 710
depth_end = 720
depth_step = 1
pitch = 12
sensor_sizex = 24
focal_length = 8
lens_x = 4
lens_y = 4
# Import elemental image
infile = os.path.join(fdir, '11.bmp')
outfile = os.path.join(fdir, 'EIRC/')
os.makedirs(outfile, exist_ok=True)
original_ei = cv2.imread(infile)
v, h, d = original_ei.shape
EI = np.zeros((v, h, d, lens_x * lens_y), dtype=np.uint8)
for y in range(lens_y):
for x in range(lens_x):
temp = cv2.imread(os.path.join(fdir, f"{y+1}{x+1}.bmp"))
EI[:, :, :, x + (y-1) * lens_y] = temp
# Reconstruction
EIy, EIx, Color = EI[:, :, :, 0].shape
# EI_VCR
time = []
for Zr in range(depth_start, depth_end+1, depth_step):
Shx = 8*round((EIx*pitch*focal_length)/(sensor_sizex*Zr))
Shy = 8*round((EIy*pitch*focal_length)/(sensor_sizex*Zr))
Img = np.zeros((EIy+(lens_y-1)*Shy, EIx+(lens_x-1)*Shx, Color), dtype=np.float64)
Intensity = np.zeros((EIy+(lens_y-1)*Shy, EIx+(lens_x-1)*Shx, Color), dtype=np.uint16)
for y in range(lens_y):
for x in range(lens_x):
Img[(y-1)*Shy:y*Shy, (x-1)*Shx:x*Shx, :] += EI[:, :, :, x+(y-1)*lens_y].astype(np.float64)
Intensity[(y-1)*Shy:y*Shy, (x-1)*Shx:x*Shx, :] += np.ones((EIy, EIx, Color), dtype=np.uint16)
elapse = time.time()
time.append(elapse)
print(f"--------------- Z = {Zr} is processed ---------------")
Fname = f"EIRC/{Zr}mm.png"
cv2.imwrite(os.path.join(fdir, Fname), Img/Intensity)
np.savetxt(os.path.join(fdir, 'EIRC/time.csv'), time, delimiter=',')
```
请注意,这只是将给定的MATLAB代码转换为等效的Python代码,但可能还需要根据实际情况对其进行进一步调整和测试。特别是在涉及图像处理的部分,可能需要使用不同的库或调整参数来实现相同的功能。
基于python使用numpy、pykinect2、opencv、time库采集Kinectv2的深度图像与彩色图像,并且每隔3秒保存一次保存名为时间值
的文件夹,在文件夹中存储一张深度图和一张彩色图,该如何编写代码实现?
很高兴回答你的问题!基于你的要求,我可以提供如下Python代码:
```Python
import numpy as np
import cv2
import ctypes
import os
import time
from pykinect2 import PyKinectV2
from pykinect2.PyKinectV2 import *
from pykinect2 import PyKinectRuntime
# 初始化Kinectv2
kinect = PyKinectRuntime.PyKinectRuntime(PyKinectV2.FrameSourceTypes_Depth | PyKinectV2.FrameSourceTypes_Color)
# 创建保存深度图和彩色图的文件夹
folder_name = str(int(round(time.time() * 1000)))
os.mkdir(folder_name)
# 循环采集深度图和彩色图,每3秒保存一次
while True:
if kinect.has_new_depth_frame() and kinect.has_new_color_frame():
# 采集深度图像
depth_frame = kinect.get_last_depth_frame()
depth_frame = depth_frame.reshape((424, 512))
depth_frame = depth_frame.astype(np.uint8)
depth_frame = cv2.applyColorMap(depth_frame, cv2.COLORMAP_JET)
# 采集彩色图像
color_frame = kinect.get_last_color_frame()
color_frame = color_frame.reshape((1080, 1920, 4))
color_frame = cv2.cvtColor(color_frame, cv2.COLOR_BGRA2BGR)
# 保存深度图和彩色图
img_name = str(int(round(time.time() * 1000)))
cv2.imwrite(folder_name + '/' + img_name + '_depth.jpg', depth_frame)
cv2.imwrite(folder_name + '/' + img_name + '_color.jpg', color_frame)
# 等待3秒
time.sleep(3)
# 释放深度图像和彩色图像
kinect._kinect.NuiSensor.DepthFrameReady -= depth_frame
kinect._kinect.NuiSensor.ColorFrameReady -= color_frame
depth_frame = None
color_frame = None
kinect._kinect.CloseHandle(kinect._kinect._depth_frame_event)
kinect._kinect.CloseHandle(kinect._kinect._color_frame_event)
```
这段代码使用numpy、pykinect2、opencv和time库来采集Kinectv2的深度图和彩色图,并且每3秒保存深度图和彩色图。具体地,代码首先初始化Kinectv2,然后创建一个保存深度图和彩色图的文件夹,接着通过循环实时采集深度图和彩色图,并且每3秒保存一次。最后,代码释放深度图像和彩色图像。
希望这个代码对你有所帮助!如果你有其他的问题,欢迎继续提问。
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